CVE Vulnerabilities


Deserialization of Untrusted Data

Published: Jun 10, 2022 | Modified: Jun 17, 2022
CVSS 3.x
CVSS 2.x
7.5 HIGH

The package gatsby-plugin-mdx before 2.14.1, from 3.0.0 and before 3.15.2 are vulnerable to Deserialization of Untrusted Data when passing input through to the gray-matter package, due to its default configurations that are missing input sanitization. Exploiting this vulnerability is possible when passing input in both webpack (MDX files in src/pages or MDX file imported as a component in frontend / React code) and data mode (querying MDX nodes via GraphQL). Workaround: If an older version of gatsby-plugin-mdx must be used, input passed into the plugin should be sanitized ahead of processing.


The product deserializes untrusted data without sufficiently verifying that the resulting data will be valid.

Affected Software

Name Vendor Start Version End Version
Gatsby Gatsbyjs * 2.14.1 (excluding)
Gatsby Gatsbyjs 3.0.0 (including) 3.15.2 (excluding)

Extended Description

It is often convenient to serialize objects for communication or to save them for later use. However, deserialized data or code can often be modified without using the provided accessor functions if it does not use cryptography to protect itself. Furthermore, any cryptography would still be client-side security – which is a dangerous security assumption. Data that is untrusted can not be trusted to be well-formed. When developers place no restrictions on “gadget chains,” or series of instances and method invocations that can self-execute during the deserialization process (i.e., before the object is returned to the caller), it is sometimes possible for attackers to leverage them to perform unauthorized actions, like generating a shell.

Potential Mitigations

  • Make fields transient to protect them from deserialization.
  • An attempt to serialize and then deserialize a class containing transient fields will result in NULLs where the transient data should be. This is an excellent way to prevent time, environment-based, or sensitive variables from being carried over and used improperly.